Autonomous learning multi-model classifier of 0-Order (ALMMo-0)

Angelov, Plamen; Gu, Xiaowei

doi:10.1109/eais.2017.7954832

Cited by 28 publications

(37 citation statements)

References 23 publications

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“…The Autonomous Learning Multiple Model (ALMMo) [13], [14] is a recently introduced autonomous learning method based on AnYa type FRB system [26] within the Empirical Data Analytics (EDA) framework [15]. It has the following form:…”

Section: Anya Frb and Almmo Ensemblementioning

confidence: 99%

“…The meaning is the order of the consequent (THEN) part of the rules which can be a singleton, integer constant which directly represents the class label (0 order), linear regression over the features (1 order), a Gaussian or another non-linear higher order output. In this paper we use 0 order ALMMo classifier [14]. An ALMMo-0 contains C individual classifiers corresponding to different classes that the dataset has, where C denotes the number of classes (in this paper, 10 C  , one for each digit).…”

Section: Anya Frb and Almmo Ensemblementioning

confidence: 99%

“…The learning process of a particular ALMMo-0 system is summarized and given in the form of pseudo code as follows [14].…”

Section: A Almmo-0 System Identificationmentioning

confidence: 99%

“…iii) Zero order AnYa type FRB [23] trained using the recent Autonomous Learning Multiple Model (ALMMo) method [13], [14] ;…”

Section: Introductionmentioning

confidence: 99%

“…The backbone of the proposed architecture is the bank of AnYa type IF … THEN rules [23]. The AnYa FRB classifier is designed using the recently introduced ALMMo [13], [14] within the Empirical Data Analytics (EDA) framework [15] which makes the learning process entirely data-driven and highly parallelizable. The proposed architecture is simple and transparent/interpretable.…”

Section: Introductionmentioning

confidence: 99%

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MICE: Multi-Layer Multi-Model Images Classifier Ensemble

Angelov

2017

2017 3rd IEEE International Conference on Cybernetics (CYBCONF)

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In this paper, a new type of fast deep learning (DL) network for handwriting recognition is proposed. In contrast to the existing DL networks the proposed approach has clearly interpretable structure that is entirely data-driven and free from user-or problem-specific assumptions. It is entirely parallelizable and very efficient. First, same fundamental image transformation techniques (rotation and scaling) that are used by other existing DL methods are used to improve the generalization. The commonly used descriptors are then used to extract the global features from the training set and based on them a bank/ensemble of zero order AnYa type fuzzy rule-based (FRB) models is built through the recently introduced Autonomous Learning Multiple Model (ALMMo) method working in parallel. The final decision about the winning class label is made by a committee on the basis of the fuzzy mixture of the trained ALMMo-0 models (where "0" stands for 0 order meaning that the consequent represent a class label, a singleton, not a regression model as in the first order). The training of the proposed MICE system is very efficient and highly parallelizable. It significantly outperforms the best known methods in terms of time and is on par in terms of precision/accuracy. Critically, it offers a high level of interpretability, transparency of the classification model, full repeatability (unlike the methods that use probabilistic elements) of the results. Moreover, it allows an evolving scenario whereby the data is provided in an incremental, online manner and the system structure is being developed in parallel with the classification which opens opportunities for online and real-time applications (on a sample by sample basis). Numerical examples from the well-known handwritten digits recognition problem (MNIST) were used and the results demonstrated the very high repeatable performance after a very short training process which is in addition to the high level of interpretability, transparency.

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